Leijun Li Purna Chandra Mishra Editors Advances in

Lecture Notes in Mechanical Engineering

Leijun LiDilip Kumar PratiharSuman ChakrabartyPurna Chandra Mishra Editors

Advances in Materials and Manufacturing EngineeringProceedings of ICAMME 2019

Lecture Notes in Mechanical Engineering

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Leijun Li • Dilip Kumar Pratihar •

Suman Chakrabarty • Purna Chandra MishraEditors

Advances in Materialsand ManufacturingEngineeringProceedings of ICAMME 2019

123

EditorsLeijun LiDepartment of Chemical and MaterialsEngineeringUniversity of AlbertaAlberta, AB, Canada

Dilip Kumar PratiharDepartment of Mechanical EngineeringIndian Institute of Technology KharagpurKharagpur, West Bengal, India

Suman ChakrabartyDepartment of Mechanical EngineeringIndian Institute of Technology KharagpurKharagpur, West Bengal, India

Purna Chandra MishraSchool of Mechanical EngineeringKIIT Deemed to be UniversityBhubaneswar, Odisha, India

ISSN 2195-4356 ISSN 2195-4364 (electronic)Lecture Notes in Mechanical EngineeringISBN 978-981-15-1306-0 ISBN 978-981-15-1307-7 (eBook)https://doi.org/10.1007/978-981-15-1307-7

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https://doi.org/10.1007/978-981-15-1307-7

Preface

This International Conference on Advances in Materials and ManufacturingEngineering (ICAMME-2019) held in the School of Mechanical Engineering insidethe beautiful campus of KIIT Deemed to be University, Bhubaneswar, Odisha,India, during 15–17 March 2019. With the pure flame of education, visionaryeducationalist Dr. Achyuta Samanta established KIIT Deemed to be University,formerly Kalinga Institute of Industrial Technology, which is a co-educationalautonomous university located at Bhubaneswar in the eastern state of Odisha, India.It was established in 1992 as an Industrial Training Institution which was developedto KIIT Deemed to be University in 2004. It was one of the youngest institutions tobe awarded the Deemed University status in India and then the University status in2004 and is recognized by Limca Book of Records. All the academic programmesare accredited by NAAC of UGC and NBA as per Washington Accord of AICTE,which are benchmarks of excellence. NAAC (government agency to evaluateuniversities) has awarded KIIT the highest grade of “A” with a CGPA of 3.36/4.KIIT Deemed to be University recently achieved the tag of Institution of Eminence(IoE) by MHRD, Government of India. The School of Mechanical Engineering,established in the year 1997, produces graduates who can meet the rapidly changingneeds of the industry which demand new skills. Current consultancy and researchand development areas of the school include residual stresses in fusion-weldedstructure, surface finish optimization by high-pressure impingement cooling andCAD modelling. Material processing technology, cleaner manufacturing technol-ogy, renewable energy, automotive engineering and quality engineering and man-agement are the other areas of interest. Research and development efforts of theschool are supported by bodies like ARDB, BRNS, AICTE and DST, Governmentof India.

The International Conference on Advances in Materials and ManufacturingEngineering (ICAMME-2019) provided an ideal platform and brought together theresearchers, scientists, engineers, industrial experts, scholars and students to shareand widen their knowledge on theoretical, numerical and experimental develop-ments in the fields of processing, manufacturing and characterization of materials.This conference offered excellent opportunities for the participants to have a direct

v

exchange of ideas and experiences, to mine potential research problems and to forgeresearch relations alongside finding partners for future collaborations. The confer-ence has invited eminent speakers from the industry and academia for deliveringkeynote lectures and plenary talks. Given the gamut of engineering challengespertaining to mechanical engineering and materials that the modern society iscurrently faced with, a holistic effort involving and transcending various disciplinesof mechanical engineering is essential. ICAMME proceedings spans over created50 topical tracks, which are well balanced in content and manageable in terms ofnumber of contributions and create an adequate discussion space for trendy topics.There were 80 oral presentations and about 40 poster presentations by participantswhich brought great opportunity to share their recent research work knowledgeamong each other graciously.

Efforts taken by peer reviewers contributed to improve the quality of manu-scripts, provided constructive critical comments, improvements and corrections tothe authors are gratefully appreciated. We are very much grateful to theInternational/National Advisory Committee, session chairs, student volunteers andadministrative assistants from the institute management who selflessly contributedto the success of this conference. Also, we are thankful to all the authors whosubmitted papers, because of which the conference became a history of success. Itwas the quality of their presentations and their passion to communicate with theother participants that really made this conference a great success.

Last but not least, we are thankful for the enormous support of Springer forsupporting us in every step of our journey towards success. Their cooperation wasnot only the strength but also an inspiration for the organizers.

Edited by:

Prof. Purna Chandra MishraProf. Leijun LiProf. Dilip Kumar PratiharProf. Suman Chakraborty

Bhubaneswar, India Prof. (Dr.) Purna Chandra MishraConference Co-chair-ICAMME 2019

[email protected]

vi Preface


Contents

Fused Deposition Modelling and Parametric Optimizationof ABS-M30 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Hemant Cherkia, Sasmita Kar, Sudhansu Sekhar Singhand Ashutosh Satpathy

Performance of Laminated Composite Turbomachinery BladesUsing Finite Element Method with Delamination . . . . . . . . . . . . . . . . . . 17Sai Mouli Makineni, P. V. Satyanarayana Yalamachili, P. Phani Prasanthi,K. Sivaji Babu and M. Mounika

Numerical Simulation of Back-Extrusion Process . . . . . . . . . . . . . . . . . . 29Ch. Bhanu Vardhan and K. Prakash Marimuthu

Effect of Two Different Dielectrics on the Machining Performanceand Their Parametric Optimization Through Response SurfaceMethodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Deepak Kumar, Shakti Kumar, Dheeraj Kumar and Nirmal Kumar Singh

Optimized Path Planning for Three-Wheeled Autonomous RobotUsing Teaching–Learning-Based Optimization Technique . . . . . . . . . . . 49Abhishek K. Kashyap and Anish Pandey

An Efficient Robotic Manipulator Trajectory Planning Using ModifiedFirefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Pradip Kumar Sahu, Gunji Bala Murali, Bijaya Kumar Khamari,Surya Narayan Panda and Bibhuti Bhusan Biswal

2D Computational Fluid Dynamics Analysis into RotationalMagnetorheological Abrasive Flow Finishing (R-MRAFF)Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Atul Singh Rajput, Deokant Prasad, Arpan Kumar Mondaland Dipankar Bose

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Effect of Red Mud on Mechanical and Microstructural Characteristicsof Aluminum Matrix Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75Priyaranjan Samal, Rishu Raj, Ravi Kumar Mandavaand Pandu R. Vundavilli

Performance Measurement in Incremental Deformation of BrassCu67Zn33 Through Soft Computing Tool . . . . . . . . . . . . . . . . . . . . . . . 83Manish Oraon, Vinay Sharma and Soumen Mandal

Parametric Analysis on Surface Roughness of Micro-channel by FiberLaser Milling on Zirconia (ZrO2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91O. F. Biswas, A. Sen, G. Kibria, Biswanath Doloiand B. Bhattacharyya

Characterization of High-Frequency Thermal Sensor for TransientTemperature Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Anil Kumar Rout, Niranjan Sahoo and Pankaj Kalita

Experimental Investigation of Waste Heat Recovery from Exhaustof Four-Stroke Diesel Engine Using Specifically ManufacturedHeat Exchanger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107Ram Thakar, Santosh Bhosle and Subhash Lahane

Influences of Feed Rate and Machining Length in Micro-millingof P-20 Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119Priyabrata Sahoo and Karali Patra

Order Tracking: Angular Domain Features Extraction Methodfor Condition Monitoring of Variable Speed . . . . . . . . . . . . . . . . . . . . . 127A. Dhal, I. Panigrahi, C. Mishra and A. K. Samantaray

Surface Roughness Characteristics of MS Rod Using Different CuttingFluids During Turning Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Pradip Mondal and Samiran Samanta

Low Velocity Impact Behavior of Closed-Cell Aluminum FoamConsidering Effect of Foam Skin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Y. M. Chordiya and M. D. Goel

Design of Optimal State Observer-Based Controller for 4-DOF PlanarManipulator Using PSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Jishnu AK, Ravi Kumar Mandava and Pandu R. Vundavilli

FEM-Based Hot Machining of Inconel 718 Alloy . . . . . . . . . . . . . . . . . . 163A. Kiran Kumar and P. Venkataramaiah

Design and Analysis of 3-DOF Spatial Serial Manipulatorfor Warehouse Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171Sumit Govind Kanpartiwar, Ravi Kumar Mandavaand Pandu R. Vundavilli

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Experimental Investigation on Mechanical Propertiesof Carbon/Bamboo/Epoxy Hybrid Laminated Composites . . . . . . . . . . . 179Y. S. Rao, B. Manikantesh, P. Sudheer Kumar and A. Yugandhar

Process Parameter Optimization in EDM: A Multi-objectiveApproach Using Metaheuristic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193Surya Narayan Panda, Ajit Kumar Pattanaik, Pradip Kumar Sahu,Prakash Kumar and Bijay Kumar Khamari

Study of Takeoff Constraints for Lifting an Agriculture PesticideSprinkling Multi-rotor System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203Umamaheswara Rao Mogili and BBVL. Deepak

Condition Monitoring of Turbine Blades with ExperimentalValidation Using FFT Analyzer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211Ravi Prakash Babu Kocharla and Raghu Kumar Bandlamudi

Outsourcing Strategies in a Two-Stage Supply Chain Modelwith Insufficient Production Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . 223Debabrata Das and Nirmal Baran Hui

Optimization of Machining Parameters to Minimize SurfaceRoughness During End Milling of AISI D2 Tool SteelUsing Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231Ravikumar D. Patel and Sanket N. Bhavsar

Robotic Assembly Sequence Generation Using Improved FruitFly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239Gunji Bala Murali, B. B. V. L. Deepak, Bibhuti Bhusan Biswaland Y. Karun Kumar

Design, Analyze and Manufacture of Hydraulic Springand Damper for an All-Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . 249Gulati Komal, Bhattacharjee Dyutiman and Panigrahi Isham

Mechanical Performance Optimization of 3D Printing Materials . . . . . . 257Shaheidula Batai and M. H. Ali

Studies on Tribological and Metal Forming Performanceof Vegetable Oil-Based Lubricants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265T. P. Jeevan and S. R. Jayaram

Analysis of Fiber Laser Micro-drilling on Quartz . . . . . . . . . . . . . . . . . 273A. Sen, Biswanath Doloi and B. Bhattacharyya

Bending Behavior of Sandwich Composite Structuresof 3D-Printed Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281M. H. Ali and Shaheidula Batai

Contents ix

Development and Fabrication of Automated Paper RecyclingMachine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289Rupesh G. Telrandhe, Dhananjay R. Ikhar and Anil C. Gawande

Stability Study of a Tapered Rotating Sandwich Beamwith Asymmetric Configuration and Variable TemperatureGradient Under Dynamic Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . 297M. Pradhan and P. R. Dash

Experimental Investigations on Activated-TIG Welding of Inconel625 and AISI 304 Alloys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311Santhiyagu Joseph Vijay, S. Mohanasundaram, P. Ramkumar,Hong Gun Kim, Alexandre Tugirumubano and Sun Ho Go

Experimental Investigation on Low-Pressure Receiver IncorporatedDomestic Refrigerator with Al2O3 Nanoparticles . . . . . . . . . . . . . . . . . . 319Vemuloori Vasu, Donthu Rakesh, K. Bintu Sumanthand V. Uma Sai Vara Prasad

Numerical Examination of Sharp V-Notches Using Notch-FlankDisplacement Collocation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329Mirzaul Karim Hussain and K. S. R. K. Murthy

Assessing the Material-Dependent Stress Distribution in FracturedBone and Orthopedic Fixing Plate by Finite Element Analysis . . . . . . . 337Y. Naidubabu, V. V. Kondaiah, Ravikumar Dumpala and B. Ratna Sunil

Production Planning in Flexible Manufacturing Systemby Considering the Multi-Objective Functions . . . . . . . . . . . . . . . . . . . . 343B. Satish Kumar, G. Janardhana Raju and G. Ranga Janardhana

Acoustic Emission-Based Grinding Wheel Condition MonitoringUsing Decision Tree Machine Learning Classifiers . . . . . . . . . . . . . . . . . 353D. S. B. Mouli and K. Rameshkumar

Impact of Collaborative Drivers of NPD on Quality Costand Customer Satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361Sudeshna Roy, Nipu Modak and Pranab K. Dan

Flow Forming of Tubes: Modeling and Optimization Using RSM,Composite Desirability Function, and TLBO . . . . . . . . . . . . . . . . . . . . . 369Prabas Banerjee, Nirmal Baran Hui, Mithilesh Dikshit and Saikat Som

Investigation on Weld Bead Geometry of AISI 201LN in GMAW-ColdMetal Transfer (CMT) Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379Vivek Singh, M. Chandrasekaran and Sutanu Samanta

Creating Productive Conditions for Electric Discharge Machiningof Non-conductive Ceramics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387Sanjeev Verma, P. S. Satsangi and K. D. Chattopadhyay

x Contents

A Critical Study of Bead-on-Plate Laser Welding of Niobium AlloyPWC-11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397Santosh Kumar Gupta, Susmita Datta, Sanjib Jaypuria,Dilip Kumar Pratihar and Partha Saha

Effect of Amplitude Oscillation on Spiking in Electron Beam Weldingof Copper Plate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405Sanjib Jaypuria, Santosh Kumar Gupta, Dilip Kumar Pratihar,Debalay Chakrabarti and M. N. Jha

Gas Tungsten Arc Welding of Inconel 825 Sheet: Study on Weld BeadGeometry and GA Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413Bishub Choudhury and M. Chandrasekaran

Stability Study of a Tapered Rotating Sandwich Beamwith Asymmetric Configuration and Variable GradientUnder Static Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421M. Pradhan, P. R. Dash and S. Mohanty

Weld Quality Prediction of PAW by Using PSO Trained RBFNN . . . . . 433Kadivendi Srinivas, Pandu R. Vundavilli and M. Manzoor Hussain

Static Loading Analysis of Connecting Rod Used in Four-Wheeler(SUV) by FEA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441Atul Singh Rajput and Mohammad Hamza

Design and Implementation of Product Embodied Riser for EnergyConservation in an Aluminum Casting Process . . . . . . . . . . . . . . . . . . . 449K. Prabhuram, V. Subrammaniyan and M. Thenarasu

Reliability, Availability and Maintainability Analysisfor Transportation Vehicles—A Case Study in APSRTC . . . . . . . . . . . . 457E. Govindarajulu and S. Sai Rakesh

A Novel Approach for Utilization of Walnut Shell Ashas Reinforcement in Aluminum Matrix Composites . . . . . . . . . . . . . . . . 463Parasa Yugandhar Babu, Phani Kumar Jogi, K. Ramakanthand P. Ravindra Babu

Analysis of Different Types of Micro Grains in Stick Welded MildSteel Plates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471Bijaya Kumar Khamari, Swapan Kumar Karak, Pradip Kumar Sahu,Surya Narayan Panda and Bibhuti Bhusan Biswal

Optimization of Process Parameters on Abrasive Jet Machiningof Ceramic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477Indranil Mandal, Thia Paul and Biswanath Doloi

Contents xi

Multi-objective Optimization of Al–Al2O3 MMC During ElectroDischarge Machining Using Desirability Function Approach . . . . . . . . . 485Manas Ranjan Panda, Sasank Sekhar Panda and H. K. Narang

Parametric Optimization of Permeability of Green Sand MouldUsing ANN and ANFIS Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495Prafulla Kumar Sahoo, Sarojrani Pattnaik and Mihir Kumar Sutar

Accurate Estimation of Mixed-Mode Stress Intensity FactorsUsing Crack Flank Displacements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503S. Sajith, K. S. R. K. Murthy and P. S. Robi

Performance Comparison of Nanofluids in Laminar Convective FlowRegion Through a Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511Md Insiat Islam Rabby, S. A. M. Shafwat Amin, Sazedur Rahman,Farzad Hossain, Mohammad Ahnaf Shahriar and A. K. M. Sadrul Islam

Microstructural and Mechanical Behaviour of Al6061/Gr/WC HybridMetal Matrix Composite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525Gangadhara Rao, Pandu R. Vundavilli and K. Meera Saheb

Experimental Investigation on Friction Stir Welding of DissimilarAlloys AA7075 and Pure Copper: Effect of Tool Materialand Process Parameters on Mechanical Properties . . . . . . . . . . . . . . . . 533B. Supraja Reddy and B. Ram Gopal Reddy

Experimental Investigations of Glycerin/Al2O3 Nanofluidin the Hydrodynamically Developing Region for AutomotiveCooling Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541Kondru Gnana Sundari, Lazarus Godson Asirvatham,Joseph John Marshal, T. Michael N. Kumar and Mona Sahu

Experimental Investigation of Twin Elliptic Orifice at DifferentNPR Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549S. Parameshwari, Pradeep Kumar, S. Thanigaiarasuand E. Rathakrishnan

Experimental Investigations on the Effect of Wheel Sizeon an Industrial Trolley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557Wilson Kumar Masepogu, Mona Sahu and Santhiyagu Joseph Vijay

Multiobjective Scheduling in Flexible Manufacturing Systemby Modified Cuckoos Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 565B. Satish Kumar, G. Janardhana Raju and G. Ranga Janardhana

Hidden Markov Modelling of High-Speed Milling (HSM) ProcessUsing Acoustic Emission (AE) Signature for Predicting ToolConditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573P. Sachin Krishnan, K. Rameshkumar and P. Krishnakumar

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Effects of the Activating Fluxes on the Properties of the Tungsten InertGas Welded Structural Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581R. S. Vidyarthy, R. Bhattacharjee, S. Mohapatra and B. B. Nayak

Powder Metallurgy Processing of Rapidly Solidified Alloyed Cast IronPowders for Machine Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589S. K. Khuntia and B. B. Pani

Effect of Debond and Randomness on Thermal Conductivitiesof Hollow Fiber Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597G. Srivalli, G. Jamuna Rani and V. Balakrishna Murthy

Study of Mechanical Property of Cenosphere and Clamshellas Filler Material in Jute Epoxy Composite . . . . . . . . . . . . . . . . . . . . . . 607Manoj Kumar, Hemalata Jena, B. Surekha and Sanjukta Sahoo

Fuzzy C-means Clustering-Based ANFIS Regression Modelingof Hybrid Laser-TIG Fabrication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617Sanjib Jaypuria, Trupti Ranjan Mahapatra, Sushanta Tripathy,Swaraj Nakhale and Santosh Kumar Gupta

Transverse Vibration and Stability of a Cracked FunctionallyGraded Rotating Shaft System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625Debabrata Gayen, Debabrata Chakraborty and Rajiv Tiwari

Influence of Treated Titania Nanoparticle on Mechanical Propertiesof Dental Nano-Composites: Manufacturing Methodand Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633Smita Rani Panda, B. C. Routara, Shanta Chakrabartyand Aswini Kumar Kar

Quality Management in Producing Engineering Graduatesby the Premier Technical Institutions: A Case Analysis . . . . . . . . . . . . . 643Papiya Chatterjee, Deepanjali Mishra, Mangal Sainand Purna Chandra Mishra

Network Repair Algorithms for Wireless Sensors and ActuatorsBased on Graph Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653Ju Jinquan, Mohammed Abdulhakim Al-Absi and Hoon Jae Lee

Cryptography Survey of DSS and DSA . . . . . . . . . . . . . . . . . . . . . . . . . 661Mohammed Abdulhakim Al-Absi, Azamjon Abdullaev,Ahmed Abdulhakim Al-Absi, Mangal Sain and Hoon Jae Lee

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671

Contents xiii

About the Editors

Leijun Li received his B.S. degree in Welding Engineering from HuazhongUniversity of Science and Technology, China and his M.S. degree in MechanicalEngineering from Xi’an Jiaotong University, China. He subsequently completed hisPh.D. in Materials Engineering at the Warren “Doc” Savage Materials Joining Labat Rensselaer Polytechnic Institute (RPI). His Materials Processing and Testing Labhas become a respected research group in the United States. In 2013, he relocatedfrom Utah to Alberta and took up his present position at the University of Alberta.

Dilip Kumar Pratihar completed his B.E. (Hons.) and M.Tech. in MechanicalEngineering at the REC Durgapur in 1988 and 1995, respectively. Presently, he is aProfessor at the IIT Kharagpur, India. His research interests include robotics, softcomputing, and manufacturing science. He has made significant contributions to thedevelopment of intelligent autonomous systems in various fields, including roboticsand manufacturing science. He has published more than 200 papers, and serves onthe editorial boards of 12 international journals. He is also a member of the expertcommittee on Advanced Manufacturing Technology, DST, Government of India.

Suman Chakrabarty is Associate Dean of SIRC, a Professor of MechanicalEngineering, and the Head of Medical Science and Technology at the IITKharagpur. His research interests include microfluidics and nanofluidics, interfacialphenomena and phase changes, and computational fluid dynamics. He received theShanti Swaroop Bhatnagar Prize in 2013, INAE Chair Professorship in 2014, andthe K.N. Seetharamu Medal and Prize from the Indian Society of Heat and MassTransfer in 2011. He is a member of many academic and professional bodies,including the ASME, ISHMT, and APS. He serves on the editorial boards ofvarious journals, and has published many papers in international journals andconference proceedings.

xv

Purna Chandra Mishra is a Professor and Dean (Research) of the School ofMechanical Engineering, Kalinga Institute of Industrial Technology, India. Hereceived his B.E. in Mechanical Engineering from Berhampur University in 2001,and his M.E. and Ph.D. in Engineering from Jadavpur University, Kolkata, in 2006and 2011, respectively. He is a member of many professional and academic bodies,and author of the book “Heat and Mass Transfer,” as well as numerous bookchapters and papers in international journals and conference proceedings. Inaddition, he holds more than 10 Indian and international patents.

xvi About the Editors

Fused Deposition Modellingand Parametric Optimizationof ABS-M30

Hemant Cherkia, Sasmita Kar, Sudhansu Sekhar Singhand Ashutosh Satpathy

Abstract In the current development of generative manufacturing industries, 3Dprinting technologies have a significant impact in the production of complex geom-etry with least time and the absence of human intercession, tools, fixtures and dies.Presently in engineering application, fused deposition modelling (FDM) has betterdemand in additive manufacturing. The improvement in design quality andmanufac-turing in FDM is based on the proper selection of principal operational parameters.This paper experimentally describes the influence of stereotypical operational vari-ables, i.e. layer thickness, raster angle, raster width, part build orientation and theirreciprocation on the precision of change in length, width, thickness, hole diameterand angle orientation of test part of acrylonitrile butadiene styrene-M30 (ABS-M30)after generated by FDM approach. It was profound that shrinkage predominatesalong the diameter of hole but an increase in dimension of length, width, thicknessand angle of inclination is more than the thirst value of the fabricated specimen.The most favourable parametric combination is followed to optimize the preciseresponses just as a change in length, width, thickness, hole diameter and angle orien-tation of build part by using a parametric design of Taguchi’s L9 orthogonal array. AsTaguchi’s methodology is not much satisfactory for steady optimal factor amalga-mation of each response Grey-Taguchi methods used to investigate the influence ofFDM parameters on multi-performance characteristics, combining all the responsesinto a single response. The correlative effect of significant factors is determined byAnalysis of Variance (ANOVA). Finally, the ANOVA on Grey relational grade indi-cates layer thickness, part build orientation and raster width which are significant.Layer thickness is themost influencing factor for part build. The percentage errors are

H. Cherkia · S. Kar (B) · S. S. Singh · A. SatpathyCAPGS, Biju Patnaik University of Technology, Rourkela, Indiae-mail: [email protected]

H. Cherkiae-mail: [email protected]

S. S. Singhe-mail: [email protected]

A. Satpathye-mail: [email protected]

© Springer Nature Singapore Pte Ltd. 2020L. Li et al. (eds.), Advances in Materials and Manufacturing Engineering, Lecture Notesin Mechanical Engineering, https://doi.org/10.1007/978-981-15-1307-7_1

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12.05, 4.55, 2.45, 3.4, 5.07 and 0.74 for change in length, width, thickness, diameter,angle and Grey relational grade, respectively.

Keywords Fused deposition modelling (FDM) · 3D printing · Acrylonitrilebutadiene styrene-M30 (ABS-M30) · Analysis of Variance (ANOVA)

1 Introduction

FDM is one among the extrusion-based additive machining process. Most signifi-cantly, the part build occurs by continuous heating and extruding the filament througha small regulated nozzle. Both the semi-molten filaments are deposited for part andsupport structure simultaneously according to the specific design of CAD model.Mostly, it assigns with ABS thermoplastics and polymer composites also. Any criti-cal complicated parts with precise dimension can be manufactured by FDM technol-ogy for different field of application, i.e. medical science, robotics, electronic items,aerospace models, investment casting moulds and patterns. CADmodel is convertedto the format of stereolithography (STL) file, and the material deposition starts fromthe outer periphery towards the inner zone by controlling the speed with temper-ature of nozzle. Gradually, the subsequent layers are generated till the completionof the build part by controlling the process parameters. Fused deposition modelling(FDM) operation is based on Taguchi design of experiments to optimize the numberof experiments, followed by ANOVA analysis and Grey relation analysis, respec-tively, to determine the relative influence of factors with effect of FDM parametersin a single response rather than individual response for dimensional precision. Theimportant operation parameters such as density of layer, direction of parts, rasterangle and raster width depend upon length, width, thickness of the ABSP 400 partswhich are assembled by FDMmethod. The contraction prevailed along the directionof the length andwidth, whereas the thickness increased from the desired value of thefabricated part is defined by Padhi et al. [1]. Kaveh et al. investigated that optimizedprinting parameters (PPs) of fabricated part had insignificant internal cavity havingleast deviation in part dimensions, hole dimension and thickness [2]. Sahu et al. pro-posed that the dimensional precision of ABSP 400 parts is manufactured by FDMtechniques and conducted the minimum experiments with Taguchi philosophy andfurther focused on single and multi-performance characteristics of responses [3].

Mahapatra and Sood proposed that they adoptedBayesian regularization to collectoptimum network architecture due to its ability to fix sum of network constants notaccording to the consideration of network size and trained ANN model using Lev-enberg–Marquardt algorithm [4]. Sood et al. studied that the process parameters thatare affecting the compressive stress significantly affect the test specimen geometry.They used quantum-behaved particle swarm optimization (QPSO) to obtain opti-mal parameters [5]. Equbal et al. focused on the five important process parameters ofFDMprocesswhich is depending upon the tensile, flexural and impact strength of testspecimen. They used bacterial foraging technique [6]. Senthilkumaran et al. studied

Fused Deposition Modelling and Parametric Optimization of ABS-M30 3

the five effective process parameter interaction calculated by Taguchi’s L27 orthogo-nal array [7]. Panda et al. defined the impact of build orientation effect, thickness oflayer and feed rate on the mechanical achievement of 3D printed PLA samples witha low cost. Mechanical response of the printed specimens is obtained by tensile andthree-point bending tests [8]. According to Sood et al., the distortion data obtainedby SL processed part is simulated by finite element method. Two significant factorsliable for part in accuracy were volumetric shrinkage and curl distortion [9]. Taguchimethod defined that the raster thickness width depends upon the layer of distortion,and the measured dimensions are presented by the LOM, FDM and SLS process [10,11]. According to Companelli et al., the surface roughness is significantly affectedby thickness and orientation of ABSP 400 plastic layer which is produced by anFDM 1650 machine [12]. Chacon et al. observed that the part orientation and partalignment depend upon the direction of the deposition [13].

2 Critical Literature Review

The immense review of the performance characteristics of FDM process is elab-orately discussed, and the critical evaluation of significant research findings areexplained. The poor surface finish leads least attention in FDM technology andwhich can be improved by controlling different parameters for dimensional preci-sion [14–17]. It is concluded that for the different dimensional part generation orany critical contour, the significance of influencing factor combination varies andaccordingly change in process parameters occurs. It also found after each perfor-mance response parameter is giving different results so to overcome this problemdifferent optimization techniques are adopted [18, 19]. Mechanical performancedevelopment is another point which can be improved by specific control of extru-sion temperature, design and inclination angle of nozzle, ambient temperature, etc[20, 21]. Though many researchers have tried to optimize the parameters to obtaindimensional accuracy from the enervative review of the literature, less attention hasbeen paid for optimizing the inclination angle of test specimen if any. So there is anopportunity to optimize the process parameters by introducing the inclination anglefor a standard specimen having one central hole with specific diameter. The designof the experiment is done based on Taguchi methodology to minimize the numberof experiments as it is robust design technology [22].

3 Experimental Methodology

The FDM process is stipulated here due to its least cost and ease of manufacturingwith less time andwithout the influence of laser in the academic field of research. Theexperiment is done by FDM process because in this process, high strength, isotropic,tailored properties of multi-materials parts are produced which can be directly used

4 H. Cherkia et al.

for making functional prototype also uniform fine-grained microstructure is pro-duced. In the FDM process ABSP 400 used as the specimen, it contains 90–100%ABS resin, (0–2%) mineral oil, (0–2%) tallow and (0–2%) wax. ABS is manufac-tured by polymerization of styrene and acrylonitrile in process of poly-butadiene(Table 1).

Fused depositionmodelling (FDM)methodmostly influenced by the input param-eters which are used in this process are part orientation, layer thickness, raster angleand raster width to find the measuring process parameters length (L), width (W ),thickness (T ), diameter (D) and inclination angle (θ ). In this experiment, two typesof input parameters are used: one is fixed parameter and another is control parameterwhich are defined in Tables 2 and 3. The input parameters and their levels are rep-resented in Table 3. Fixed parameters depend upon specific FDM machine set-up.The part build generation is along the vertical direction z-axis, as x- and y-axis arehorizontally placed on build platform.

The experimental study for four control parameters mostly necessitates 81 (34)experiments in classicalDOEbut similar statistical result can be obtained by adoptingTaguchi DOE methods. Considering four factors with 3-level, there are 8 degrees offreedom, and a suitable orthogonal array L9 (34) is established. The designed arraybuilds up with four columns for assigning factors and nine rows designating thetrial or experiment conditions. The L9 orthogonal array can contain a maximum no.of four factors and each factor at three levels. The experimental set-up having four

Table 1 Mechanicalproperties of ABSP 400material

Mechanicalproperties

Unit Extruded Moulded

Density g/cm3 0.350–1.26 1.02–1.17

Rockwellhardness

HRC 90.0–121 68.00–115

UTS MPa 27.00–52.00 28.00–49.00

Yield strength MPa 20.0–62.0 13.00–65.00

Modulus ofelasticity

GPa 1.52–6.10 1.00–2.65

Elongationyield

%(percentage)

0.620–30.0 1.70–6.00

Table 2 Fixed parameters Parameter Value Unit

Style of part fill Perimeter/style –

Width of counter 0.4064 mm

Style of interior part Normal solid –

Visible surface Normal raster –

Shrink factor in XY and Z 1.0038 –

Air gap of perimeter/raster 0 mm


Table 3 Control parametersand their level

Parameters Symbols Levels

1 2 3

Thickness oflayer

A 0.127 0.178 0.254

Orientation B 0 15 30

Raster angle C 0 30 60

Raster width D 0.4064 0.4654 0.5064

factors without any interactions, then factors can be placed arbitrarily in any column.Therefore, in this case layer thickness (A) is assigned to first column, orientation (B)is assigned to column 2, raster angle (C) is assigned to column 3 and raster width (D)is assigned to column 4. After fabricating the model (ABS-M30) by Fortus 400mcmachine set-up, the measurement of each dimension is obtained. Horizontal, verticalandhole dimensions aremeasured usingMitutoyodigitalVernier calliper having leastcount of 0.01 mmwhere external and internal distances can be precisely determined.Internal jaws are used for measuring internal dimensions of holes and cavities, i.e.length (L), width (W ) and thickness (T ). For measuring hole diameter (d), internaljaws are adjusted carefully until they touch the internal surface of hole. Angle ofinclination of the specimen is measured by optical profile projector, an optical devicewith enlarge image. It contains a light source, condenser lens, projection lens andscreen. A beam of light from the light source is passed through the condenser lens andprojection lens and falls on the screen. The work piece will be placed in between thelight source and condenser lens. A shadow image of the work piece will be created,while the work piece is placed (Figs. 1 and 2, Table 4).

Fig. 1 Enlarged views of specimen on optical profile projector screen

6 H. Cherkia et al.

Fig. 2 Fabrication of FDM processed part

Table 4 L9 OA (S/N ratio)

No of exp Aspects S/N ratio

A B C D �L �W �T �D �θ

1 1 1 1 1 22.3078 32.6404 16.4781 20.9151 7.3508

2 1 2 1 2 26.6198 23.9673 11.9261 13.4188 13.4732

3 1 3 1 3 22.6939 33.9794 15.5630 12.2759 10.2289

4 2 1 2 2 23.9673 21.9382 15.2223 18.9128 12.2878

5 2 2 2 3 13.8358 22.6939 11.0568 13.6945 11.2767

6 2 3 2 1 12.6404 23.9673 9.9879 12.0411 13.4324

7 3 1 3 3 16.2872 17.0774 12.2759 9.0363 11.1191

8 3 2 3 1 20.5992 19.7151 11.1608 10.1727 5.1927

9 3 3 3 2 13.9794 17.2867 8.8739 10.6527 1.1004

Relative change in dimensions of the measuring process parameters is calculatedas per the following equation

�X = |X − XCAD| (1)

In Taguchi methodology, the significant use of S/N ratio is to findthe required values of differences between the performance charac-teristics. The inspection of S/N ratio is based on three categories:(i) the lower the better, (ii) the higher the better and (iii) the more nominalthe better. Objective of experiment plan is to reduce the relative change in length(�L), width (�W ), thickness (�T ), diameter (�D) and angle (�θ ) as small aspossible. Therefore, “smaller the better” quality characteristic is considered. For


“smaller the better” quality characteristic, S/N ratio (η) is expressed by Eq. (2) [23].

[S/N ]SB = −10log(MSDSB) (2)

where MSDSB = [y1+ 2y2+ 2y3+ 2 . . . yn2]/n

4 Grey Relational Analysis

Grey relational analysis (GRA) is an impacting measurement method in Grey theorythat analyses uncertain relations among factors and interactions in a given system.Theaim is to determine the optimum factor setting to satisfy all the five performance char-acteristics simultaneously. It is actually a measurement of the absolute value of thedata difference between sequences, and it could be used to measure the approximatecorrelation between sequences. The Taguchi method is best suited for optimizationof a single performance characteristic, whereas Grey-based Taguchi (Grey-Taguchi)combines all performance characteristics (objectives) considered in the study into asingle value that can be used as the single characteristic in optimization problems. InGrey relational analysis (GRA), the experimental results of responses are normalizedat first in the range between 0 and 1 due to different measurement units.

5 Analysis of Variance (ANOVA)

The analysis of data is done by usingMinitabR17 software at 95% level of confidencevalue and the relation between the factors are resolved by the ANOVA technique.This technique is used to analyse the difference between group mean also usedapportioning the variance of an output to a different input. The total degrees offreedom of four parameters and each contain three levels of eight which is equivalentto the experimental DF. The ANOVA results of�L,�W,�T,�D and�θ are shownin Tables 5, 6, 7 and 8, respectively, and also using this value, main effect plots areprepared which show the minimizing response and S/N ratio.

6 Results and Discussions

In this section, there will be an analysis result obtained from the experiment donethrough FDM process, and all the resulting values show that there is a shrinkageoccur in diameter of hole, but length,width, thickness and angle of inclination value isconsistently greater than themodel value those prepared byCADsoftware. Shrinkagealong the diameter of hole may be obtained due to the progress of closer stressesin material deposition time. To minimize this error of part dimension, Grey-Taguchi

8 H. Cherkia et al.

Table 5 ANOVA table for �L (change in length)

Sources DF Sum of squares Variance F-value P-value P (%)

A 2 97.72 48.86 3.88 0.025 46.72

B 2 35.07 17.54 1.39 0.418 16.76

C* 25.21 12.60

D 2 51.15 25.57 2.03 0.330 24.45

Error 2 25.21 12.60 12.05

Total 8 209.15

*Signifies the pulled out parameter based on Grey Taguchi method

Table 6 ANOVA table for �W (change in width)


A 2 225.24 112.618 17.02 0.056 77.47

B* 13.24 6.618

C 2 32.28 16.140 2.44 0.291 11.10

D 2 19.97 9.987 1.51 0.399 6.86

Error 2 13.24 6.618 4.5

Total 8 290.72


Table 7 ANOVA table for �T (change in thickness)


A 2 23.425 11.7123 16.95 0.056 41.58

B 2 20.888 10.4440 15.11 0.062 37.07

C* 1.382 0.6912

D 2 10.638 5.3191 7.70 0.115 18.88

Error 2 1.382 0.6912 2.45

Total 8 56.333


Table 8 ANOVA table for �D (change in diameter)


A 2 55.89 27.944 3.8 0.205 43.99

B 2 36.94 18.471 2.56 0.281 29.07

C 14.46 7.202

D 2 19.81 9.903 1.38 0.421 15.59

Error 2 14.40 7.202 11.33

Total 8 127.04


Table 9 ANOVA table for �θ (change in inclination angle)


A 2 67.218 33.690 9.53 0.095 48.33

B* 7.051 3.525

C 2 8.690 4.345 1.23 0.448 6.24

D 2 56.097 28.049 7.96 0.112 40.34

Error 2 7.051 3.525 5.07

Total 8 139.056


method is used because this is an effective method for determining the effectivenessof experimental design by relating the effect of the input parameters with that to theoutput parameters. Taguchi method uses a statistical tool to measure performanceknown as signal-to-noise ratio (S/N). S/N ratio consists of both mean and variabilityof performance characteristics. Themain objective of this paper is to reduce the valueof change in length (�L), width (�W ), thickness (�T ), diameter (�D) and angle(�θ ), and those things are discussed.

Tables 5, 6, 7, 8 and 9 show the observed significant factors for different responsesare different.

7 Main Effect Plot for S/N Ratios

These plots are shown the relation between input of FDM parameter and signal-to-noise ratio which obtain by the help of Minitab software. It responses mean for eachfactor level connected by a line and indicates the response in a different way withvarying levels and factors independently (Figs. 3, 4, 5, 6 and 7).

From the plot, the most significant factors of combination on process parametersare obtained A, B, D for �L, A, C, D for �W, A, B, D for �T and �D, A, C, D for�θ , respectively.

8 Grey Relational Analysis for Dimensional Accuracy

For improvement in part dimensional accuracy, Grey-Taguchi method is used. InGrey-Taguchi method, the five input parameters are �L, �W, �T, �D and �θ , andsmaller is the better type response which is considered. It represents a combinationof all the response into a single response (Fig. 8).

Exclusive experimental investigation was carried out using Taguchi’s methodfor different responses individually. Grey relational optimization was to find thesignificant factors affecting FDM processed part at different levels, and ANOVA

10 H. Cherkia et al.

Fig. 3 Main effect plot for S/N ratios (�L)

Fig. 4 Main effect plot for S/N ratios (�W )


Fig. 5 Main effect plot for S/N ratios (�T )

Fig. 6 Main effect plot for S/N ratios (�D)


Fig. 7 Main effect plot for S/N ratios (�θ)

Fig. 8 Effects of FDM parameters on the multi-performance characteristics


Table 10 Contribution percentage

Factors Contribution (%)

�L �W �T �D �θ Grey relational grade

Layer thickness (A) 46.72 77.47 41.58 43.99 48.33 70.08

Orientation (B) 16.76 – 37.07 29.07 – 16.66

Raster angle (C) – 11.10 – 6.24 –

Raster width (D) 24.45 6.86 18.88 15.59 40.34 12.49

Error (%) 12.05 4.55 2.45 3.4 5.07 0.74

was calculated to predict the percentage contribution of different factors at differentlevels. The percentage error for the difference in length, width, thickness and angle byGrey relational analysis is obtained: 12.05, 4.55, 2.45, 3.4, 5.07 and0.74, respectively.This result shows that all the responses are combining into a single response usingGrey-Taguchimethod. It ismore effective than considering the responses individuallyby the Taguchi method (Table 10).

9 Conclusions

In this current work, the FDM technique was used to fabricate acrylonitrile butadienestyrene (ABS-M30) parts. The process parameters were optimized at a common levelsetting using Grey-based Taguchi with DOE of Taguchi’s philosophy to obtain theminimum changes in length, width, hole diameter, angle of orientation and thicknessfrom the desired values simultaneously. Based on experimental studies carried outfor optimization of the FDM process parameters, some of the important findings areobtained.

The height of the part considered in this work at maximum orientation of 30will be 13.031 mm. If it is sliced with minimum thickness of 0.127 mm, a totalof 102.60 slices will be required by simple arithmetic. It is found that shrinkageis dominant along the diameter of hole and of test part whereas the length, width,thickness and angle orientation are always more than the desired value. Ultimately,thickness of layer 0.255 mm, orientation of part 30°, raster angle of 30° and rasterwidth of 0.4046 mm are optimal factor settings for developing all characteristics ofperformance concurrently. The contribution of layer thickness is more compared toall other measured constants for governing the change in dimension of FDM builtpart. Effect of process parameters on dimensional accuracy is studied on flat andcircular profiles only.


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Leijun Li Purna Chandra Mishra Editors Advances in